Chapter 6: Reinforcement Learning in the Real World – Building Intelligent Agents to Complete Your To-Dos
An RL Agent needs to interact with the environment to learn and train. Training RL Agents for real-world applications usually comes with physical limitations and challenges. This is because the Agent could potentially cause damage to the real-world system it is dealing with while learning. Fortunately, there are a lot of tasks in the real world that do not necessarily have such challenges, and yet can be very useful for completing the day-to-day real-world tasks that are available in our To-Do lists!
The recipes in this chapter will help you build RL Agents that can complete tasks on the internet, ranging from responding to annoying popups, ...
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